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Data Science and Risk Analytics in Finance and Insurance: Financial Models and Statistical Methods - Rilegato
Sinossi
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.
Key Features:
- Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.
- Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.
- Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.
- Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.
- Includes supplements and exercises to facilitate deeper comprehension.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Tze Leung Lai is the Ray Lyman Wilbur Professor and Professor of Statistics at Stanford University. He received the COPSS Presidents' Award in 1983. He has published extensively on sequential statistical analysis and a wide range of applications in the biomedical sciences, engineering, and finance.
Haipeng Xing is a Professor of Applied Mathematics and Statistics at State University of New York, Stony Brook. His research interests include sequential statistical methods and its applications, econometrics, quantitative finance, and recursive methods in macroeconomics.
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Data Science and Risk Analytics in Finance and Insurance
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.Key Features:Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.Includes supplements and exercises to facilitate deeper comprehension. 464 pp. Englisch. Codice articolo 9781439839485
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Risk Analytics and Management in Finance and Insurance
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Active Risk Management
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Condizione: New. pp. 350 This item is printed on demand. Codice articolo 55095521
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Risk Analytics and Management in Finance and Insurance
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tze Leung Lai is a professor of statistics at Stanford University in California. Haipeng Xing is an assistant professor of applied mathematics and statistics at the State University of New York, Stony Brook.Tze Leung Lai is the R. Codice articolo 254938520
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Data Science and Risk Analytics in Finance and Insurance: Financial Models and Statistical Methods
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Hardcover. Condizione: Brand New. 1st edition. 350 pages. 6.14x1.24x9.21 inches. In Stock. This item is printed on demand. Codice articolo __1439839484
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Data Science and Risk Analytics in Finance and Insurance (Chapman and Hall/CRC Financial Mathematics Series)
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Data Science and Risk Analytics in Finance and Insurance (Chapman and Hall/CRC Financial Mathematics Series)
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Data Science and Risk Analytics in Finance and Insurance
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.Key Features:Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.Includes supplements and exercises to facilitate deeper comprehension. Codice articolo 9781439839485
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